Trainer.lua


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local Trainer = torch.class('nn.Trainer')

function Trainer:__init()
   self.learningRate = 0.01
   self.learningRateDecay = 0
   self.maxIteration = 25
end

function Trainer:train(dataset)
end

function Trainer:share(mlp, ...)
   for i,v in ipairs(arg) do
      if self[v] ~=nil then self[v]:set(mlp[v]) end
   end
end

function Trainer:clone(...)
   local f = torch.MemoryFile("rw"):binary()
   f:writeObject(self)
   f:seek(1)
   local clone = f:readObject()
   f:close()
   if select('#',...) > 0 then
      clone:share(self,...)
   end
   return clone
end